Cardiac magnetic resonance imaging (CMR) is a valuable tool that provides important information for diagnosis and evaluation of cardiac anatomic abnormalities, and cardiovascular disease (Frangi A F, Niessen W J, Viergever M A. Three-dimensional modeling for functional analysis of cardiac images, a review. Medical Imaging, IEEE Transactions on 2001; 20(1):2-5). CMR is a safe modality that does not require ionizing radiation or iodinated contrast but delivers images with high spatial and temporal resolution (Yuan C, Kerwin W S, Ferguson M S, et al. Contrast-enhanced high resolution MRI for atherosclerotic carotid artery tissue characterization. Journal of Magnetic Resonance Imaging 2002; 15(1): 62-67; Lima J A, Desai M Y. Cardiovascular magnetic resonance imaging: current and emerging applications. Journal of the American College of Cardiology 2004; 44(6):1164-1171). One important aspect of CMR imaging is its potential for segmentation of the cardiac chambers to determine clinical information such as ejection fraction and chamber volumes (Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis 2009; 13(4): 543-563). Currently many of the commercially available software platforms for CMR post-processing either provide suboptimal automated segmentation or require a substantial amount of manual segmentation support from the user, resulting in significant methodological variability (Janik M, Cham M D, Ross M I, et al. Effects of papillary muscles and trabeculae on left ventricular quantification: increased impact of methodological variability in patients with left ventricular hypertrophy. Journal of hypertension 2008; 26(8):1677-1685). Additionally, manual segmentation is time consuming, and requires dedicated operator training that makes it inefficient due to the extent of information in CMR images.
Most cardiac segmentation techniques treat “2D segmentation” and “3D multiplanar reconstruction” as two separate processes (Jolly M-P. Automatic segmentation of the left ventricle in cardiac MR and CT images. International Journal of Computer Vision 2006; 70(2): 151-163). These processes achieve volumetric reconstruction by first applying a 2D segmentation approach independently for each slice, and then volumizing these 2D segmented image stacks into 3D objects. This procedure only considers volumizing a particular stack. Therefore, some important details of the object would be lost during the procedure; thus the resultant objects usually possess rough surfaces.
Although there are some methods to automate consecutive “2D segmentation” and “3D multiplanar reconstruction” steps, this approach fails to exploit the benefit of a true, 3D volumizing technique. Additionally, most segmentation approaches in 2D cannot readily handle cases where an object of interest (e.g., papillary muscles) appears to be separated into several cross-sections (i.e., non-convex object). This separation and discontinuity commonly can be seen in CMR images, which incur further challenges in 2D segmentation.
The need for an efficient, accurate, and automated segmentation method has stimulated a large body of work in automated 3D CMR segmentation. Among these studies, early attempts at thresholding (Goshtasby A, Turner D A. Segmentation of cardiac cine MR images for extraction of right and left ventricular chambers. Medical Imaging, IEEE Transactions on 1995; 14: 56-64) were followed by the popular pixel classification (Pednekar A, Kurkure U, Muthupillai R, Flamm S, Kakadiaris I A. Automated left ventricular segmentation in cardiac MRI. Biomedical Engineering, IEEE Transactions on 2006; 53(7): 1425-1428; Lynch M, Ghita O, Whelan P F. Automatic segmentation of the left ventricle cavity and myocardium in MRI data. Computers in Biology and Medicine 2006; 36(4): 389-407), active contour approaches (Xu C, Pham D L, Prince J L. Image segmentation using deformable models. Handbook of medical imaging 2000; 2:129-174; Grosgeorge D, Petitjean C, Caudron J, Fares J, Dacher J-N. Automatic cardiac ventricle segmentation in MR images: a validation study. International journal of computer assisted radiology and surgery 2011; 6(5): 573-581) and region based approaches (Grosgeorge et al. (supra); Mule J, Bone R, Makris P, Cardot H. Segmentation and tracking of the left ventricle in 3D MRI sequences using an active surface model. In Computer-Based Medical Systems, Twentieth IEEE International Symposium on; 2007. p. 257-262). However, none of these singular approaches has resulted in an accurate and fast segmentation algorithm that requires no prior statistical model.